Exploring the Effects of Fruit Brand Names on Consumer Preferences: A Case Study of Apple Consumer Behavior
Bibliographic record
Abstract
ABSTRACT Despite the recognized impact of brand names on consumer behavior, limited research has specifically explored how brand names affect customers' fruit quality perceptions and preferences. The main objective of this study was to investigate the effects of apple brand names, as a case study, on consumers' brand recognition and preferences, considering their purchase and consumption behaviors and demographics. Consumer preferences toward four apple brand name categories were specifically investigated: sensory component names (SCN), metaphoric names (MN), non‐metaphoric names (NMN), and innovative spelling names (ISN). A total of 526 Canadian residents participated in an online survey, and 517 submitted responses were accepted. Names from the SCN category were liked the most and disliked the least. Names from the MN category were disliked less than those from NMN and ISN categories. Names from the ISN category were disliked the most. Overall, the results highlighted the significance of brand names, with SCN being associated with greater recognition and preference.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".